Bibliographic record
Abstract
The wide-open prairies of North America captivated the first European settlers while they migrated west. But it turns out that human eyes are not best adapted to take in the landscape's colossal scale. Bret Moore, Esteban Fernandez-Juricic and colleagues from Purdue University, the United States Department of Agriculture and Cornell University explain that species that dwell in expansive landscapes often have a specialised visual adaptation where nerve cells are distributed in a horizontal band across the retina, allowing them to see clearly over a wide angular range. According to the team, the Canada goose has this particular visual adaptation; however, instead of being horizontally oriented, the goose's visual streak is inclined at an angle to the horizon. With this in mind, the team analysed the distribution of the light-sensitive cone cells to determine whether specific regions of the bird's retina are specialised for viewing different regions of the visual field (p. 3442).Collecting the retinas of adult geese and measuring the absorbance of the pigment in the four types of cone cell that they identified, the team found that the photoreceptors absorbed light strongly at violet (409 nm), blue (458 nm), green (509 nm) and yellow (580 nm) wavelengths. Unlike small songbirds, which have an ultraviolet-sensitive photoreceptor, the geese are only sensitive to visible wavelengths, akin to other large birds. Plotting the distributions of the cones across the retina, the scientists found that the photreceptors were most densely packed where they coincided with the inclined visual streak, and they suspect that the distribution allows the geese to simultaneously see the sky and ground clearly. In addition, the team calculated the visual contrasts between different sections of the bird's plumage, and found that the patches that are most conspicuous to the birds also happen to be the regions that the birds track while flocking.So the Canada goose's vision is well suited to its flocking lifestyle, and allows it to survey its surroundings. However, the team adds, ‘Our results show that the Canada goose visual system has features that make it rather different from other vertebrates living in open habitats’, and they hope to discover whether other open-habitat birds share any of the goose's unique visual system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".